FREQuency-resolved brain Network Estimation via Source Separation (FREQ-NESS)

Abstract

The brain is a dynamic system whose network organization is often studied by focusing on specific frequency bands or anatomical regions, leading to fragmented insights, or by employing complex and elaborate methods that hinder straightforward interpretations. To address this issue, a new analytical pipeline named FREQuency-resolved Network Estimation via Source Separation (FREQ-NESS) is introduced. This is designed to estimate the activation and spatial configuration of simultaneous brain networks across frequencies by analyzing the frequency-resolved multivariate covariance between whole-brain voxel time series. FREQ-NESS is applied to source-reconstructed magnetoencephalography (MEG) data during resting state and isochronous auditory stimulation. Results reveal simultaneous, frequency-specific brain networks during resting state, such as the default mode, alpha-band, and motor-beta networks. During auditory stimulation, FREQ-NESS detects: (1) emergence of networks attuned to the stimulation frequency, (2) spatial reorganization of existing networks, such as alpha-band networks shifting from occipital to sensorimotor areas, (3) stability of networks unaffected by auditory stimuli. Furthermore, auditory stimulation significantly enhances cross-frequency coupling, with the phase of attuned auditory networks modulating the gamma band amplitude of medial temporal lobe networks. In conclusion, FREQ-NESS effectively maps the brains spatiotemporal dynamics, providing a comprehensive view of brain function by revealing simultaneous, frequency-resolved networks and their interaction.